Skip to main content
Springer Nature Link
Log in

Propagation of Fake News on Social Media: Challenges and Opportunities

  • Conference paper
  • First Online:
Computational Data and Social Networks (CSoNet 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12575))

Included in the following conference series:

Abstract

Fake news, particularly with the speed and reach of unverified/false information dissemination, is a troubling trend with potential political and societal consequences, as evidenced in the 2016 United States presidential election, the ongoing COVID-19 pandemic, and the ongoing protests. To mitigate such threats, a broad range of approaches have been designed to detect and mitigate online fake news. In this paper, we systematically review existing fake news mitigation and detection approaches, and identify a number of challenges and potential research opportunities (e.g., the importance of a data sharing platform that can also be used to facilitate machine/deep learning). We hope that the findings reported in this paper will motivate further research in this area.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Ahmed, S.: Who inadvertently shares deepfakes? analyzing the role of political interest, cognitive ability, and social network size. Telematics Inform, p. 101508 (2020)

    Google Scholar 

  2. Wasim, A., Josep, V.-A., Joseph, D., Seguí, F.L.: Dangerous messages or satire? analysing the conspiracy theory linking 5G to COVID-19 through social network analysis. J. Med. Internet Res. (2020)

    Google Scholar 

  3. Oberiri, D.A., Omar, B.: Fake news and covid-19: modelling the predictors of fake news sharing among social media users. Telematics Inform. p. 101475 (2020)

    Google Scholar 

  4. Mandrita, B., Junghee, L., Choo, K.K.R.: A blockchain future for internet of things security: a position paper. Digital Commun. Netw. 4(3), 149–160 (2018)

    Google Scholar 

  5. Bondielli, A., Marcelloni, F.: A survey on fake news and rumour detection techniques. Inf. Sci. 497, 38–55 (2019)

    Article  Google Scholar 

  6. Samara, C., et al.: Detection of bots and cyborgs in twitter: a study on the chilean presidential election in 2017. In: International Conference on Human-Computer Interaction, pp. 311–323. Springer (2019)

    Google Scholar 

  7. Chesney, R., Citron, D.: Deepfakes and the new disinformation war: the coming age of post-truth geopolitics. Foreign Aff. 98, 147 (2019)

    Google Scholar 

  8. Gellin, B.: Why vaccine rumours stick-and getting them unstuck. The Lancet 396(10247), 303–304 (2020)

    Article  Google Scholar 

  9. Saqib, H., Wazir, Z.K., Imran, M., Choo, K.K.R., Shoaib, M.: Have you been a victim of covid-19-related cyber incidents? survey, taxonomy, and mitigation strategies. IEEE Access, 8, 124134–124144 (2020)

    Google Scholar 

  10. Sawinder, K., Parteek, K., Kumaraguru, P.: Automating fake news detection system using multi-level voting model. Soft Comput. 1–21 (2019)

    Google Scholar 

  11. Dhruv, K., Jaipal, S.G., Gupta, M., Varma, V.: Mvae: multimodal variational autoencoder for fake news detection. In: The World Wide Web Conference, pp. 2915–2921 (2019)

    Google Scholar 

  12. David, M.J., et al.: The science of fake news. Science 359(6380), 1094–1096 (2018)

    Google Scholar 

  13. Yang, L., Yi-Fang, B.W.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  14. Yang Liu and Yi-Fang Brook Wu: Fned: a deep network for fake news early detection on social media. ACM Trans. Inf. Syst. (TOIS) 38(3), 1–33 (2020)

    Google Scholar 

  15. Feyza Altunbey Ozbay and Bilal Alatas: Fake news detection within online social media using supervised artificial intelligence algorithms. Physica A: Stat. Mech. Appl. 540, 123174 (2020)

    Article  Google Scholar 

  16. Neha, P., Eric, A.C., Hourmazd, H., Gunaratne, K.: Social media and vaccine hesitancy: new updates for the era of covid-19 and globalized infectious diseases. Human Vaccines & Immunotherapeutics, pp. 1–8 (2020)

    Google Scholar 

  17. Jorge, R., et al.: A one-class classification approach for bot detection on twitter. Comput. Secur. 91, 101715 (2020)

    Google Scholar 

  18. Natali, R., Seo, S., Liu, Y.: CSI: a hybrid deep model for fake news detection. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 797–806 (2017)

    Google Scholar 

  19. Giovanni, C.S., Munif, I.M., Jake, R.W.: Detecting social bots on facebook in an information veracity context. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 463–472 (2019)

    Google Scholar 

  20. Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M., Liu, Y.: Combating fake news: a survey on identification and mitigation techniques. ACM Trans. Intell. Syst. Technol. (TIST) 10(3), 1–42 (2019)

    Article  Google Scholar 

  21. Kai, S., Limeng, C., Suhang, W., Lee, D., Liu, H.: Defend: explainable fake news detection. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 395–405 (2019)

    Google Scholar 

  22. Shu, K., Mahudeswaran, D., Liu, H.: Fakenewstracker: a tool for fake news collection, detection, and visualization. Comput. Math. Organ. Theory 25(1), 60–71 (2019)

    Article  Google Scholar 

  23. Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media: a data mining perspective. ACM SIGKDD Explorations Newsletter 19(1), 22–36 (2017)

    Article  Google Scholar 

  24. Sebastian, T., Adish, S., Manuel, G.R., Arpit, M., Andreas, K.: Fake news detection in social networks via crowd signals. In: Companion Proceedings of the The Web Conference 2018, pp. 517–524 (2018)

    Google Scholar 

  25. William, Y.W.: liar, liar pants on fire: a new benchmark dataset for fake news detection. arXiv preprint arXiv:1705.00648 (2017)

  26. Yang, S., Shu, K., Wang, S., Renjie, G., Fan, W., Liu, H.: Unsupervised fake news detection on social media: a generative approach. Proc. AAAI Conf. Artif. Intell. 33, 5644–5651 (2019)

    Google Scholar 

  27. Reza, Z., Xinyi, Z., Kai, S., Huan, L.: Fake news research: theories, detection strategies, and open problems. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3207–3208 (2019)

    Google Scholar 

  28. Xichen, Z., Ali, A.G.: An overview of online fake news: characterization, detection, and discussion. Inf. Process. Manage. 57(2), 102025 (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer Science, Canadian Institute for Cybersecurity, University of New Brunswick, Fredericton, Canada

    Saqib Hakak

  2. Faculty of CS and IS, Jazan University, Jazan, Saudi Arabia

    Wazir Zada Khan

  3. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

    Sweta Bhattacharya & G. Thippa Reddy

  4. Department of Information Systems and Cyber Security, University of Texas at San Antonio, San Antonio, TX, 78249-0631, USA

    Kim-Kwang Raymond Choo

Authors
  1. Saqib Hakak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wazir Zada Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sweta Bhattacharya
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. G. Thippa Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kim-Kwang Raymond Choo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kim-Kwang Raymond Choo .

Editor information

Editors and Affiliations

  1. University of South Florida, Tampa, FL, USA

    Sriram Chellappan

  2. The University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

  3. New Jersey Institute of Technology, Newark, NJ, USA

    NhatHai Phan

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, KK.R. (2020). Propagation of Fake News on Social Media: Challenges and Opportunities. In: Chellappan, S., Choo, KK.R., Phan, N. (eds) Computational Data and Social Networks. CSoNet 2020. Lecture Notes in Computer Science(), vol 12575. Springer, Cham. https://doi.org/10.1007/978-3-030-66046-8_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-66046-8_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66045-1

  • Online ISBN: 978-3-030-66046-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics